{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your interests.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
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      "Done extracting features for 48000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "iteration 2700 / 3000: loss 9.000000\n",
      "iteration 2800 / 3000: loss 9.000001\n",
      "iteration 2900 / 3000: loss 9.000001\n",
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.101265 val accuracy: 0.112000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.112980 val accuracy: 0.107000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.416041 val accuracy: 0.415000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.145469 val accuracy: 0.150000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.413918 val accuracy: 0.418000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.400286 val accuracy: 0.382000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.413776 val accuracy: 0.415000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.412939 val accuracy: 0.412000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.337367 val accuracy: 0.338000\n",
      "best validation accuracy achieved during cross-validation: 0.418000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "for i in range(0,3):\n",
    "    for j in range(0,3):\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train_feats, y_train, learning_rate=learning_rates[i],\\\n",
    "                              reg=regularization_strengths[j],num_iters=3000, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train_feats)\n",
    "        y_val_pred = svm.predict(X_val_feats)\n",
    "        trn_accu=np.mean(y_train == y_train_pred)\n",
    "        val_accu=np.mean(y_val == y_val_pred)\n",
    "        results[(learning_rates[i],regularization_strengths[j])]=(trn_accu,val_accu)\n",
    "        if val_accu > best_val:\n",
    "            best_val=val_accu\n",
    "            best_svm=svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.425\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "#显示错分的图像\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302586\n",
      "iteration 100 / 1000: loss 2.303276\n",
      "iteration 200 / 1000: loss 2.113610\n",
      "iteration 300 / 1000: loss 1.779652\n",
      "iteration 400 / 1000: loss 1.692518\n",
      "iteration 500 / 1000: loss 1.510110\n",
      "iteration 600 / 1000: loss 1.378024\n",
      "iteration 700 / 1000: loss 1.517078\n",
      "iteration 800 / 1000: loss 1.441774\n",
      "iteration 900 / 1000: loss 1.412619\n",
      "iteration 0 / 1000: loss 2.302586\n",
      "iteration 100 / 1000: loss 1.829125\n",
      "iteration 200 / 1000: loss 1.373397\n",
      "iteration 300 / 1000: loss 1.484702\n",
      "iteration 400 / 1000: loss 1.324823\n",
      "iteration 500 / 1000: loss 1.322589\n",
      "iteration 600 / 1000: loss 1.289925\n",
      "iteration 700 / 1000: loss 1.238025\n",
      "iteration 800 / 1000: loss 1.371825\n",
      "iteration 900 / 1000: loss 1.197441\n",
      "iteration 0 / 1000: loss 2.302586\n",
      "iteration 100 / 1000: loss 1.429066\n",
      "iteration 200 / 1000: loss 1.504527\n",
      "iteration 300 / 1000: loss 1.350555\n",
      "iteration 400 / 1000: loss 1.385750\n",
      "iteration 500 / 1000: loss 1.276386\n",
      "iteration 600 / 1000: loss 1.249144\n",
      "iteration 700 / 1000: loss 1.332338\n",
      "iteration 800 / 1000: loss 1.257266\n",
      "iteration 900 / 1000: loss 1.073384\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.302734\n",
      "iteration 200 / 1000: loss 2.275788\n",
      "iteration 300 / 1000: loss 1.905918\n",
      "iteration 400 / 1000: loss 1.706859\n",
      "iteration 500 / 1000: loss 1.689471\n",
      "iteration 600 / 1000: loss 1.438302\n",
      "iteration 700 / 1000: loss 1.408202\n",
      "iteration 800 / 1000: loss 1.418736\n",
      "iteration 900 / 1000: loss 1.421771\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 1.894109\n",
      "iteration 200 / 1000: loss 1.389233\n",
      "iteration 300 / 1000: loss 1.275065\n",
      "iteration 400 / 1000: loss 1.236911\n",
      "iteration 500 / 1000: loss 1.418142\n",
      "iteration 600 / 1000: loss 1.376351\n",
      "iteration 700 / 1000: loss 1.274835\n",
      "iteration 800 / 1000: loss 1.286811\n",
      "iteration 900 / 1000: loss 1.367729\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 1.587000\n",
      "iteration 200 / 1000: loss 1.375771\n",
      "iteration 300 / 1000: loss 1.489385\n",
      "iteration 400 / 1000: loss 1.414227\n",
      "iteration 500 / 1000: loss 1.340694\n",
      "iteration 600 / 1000: loss 1.250502\n",
      "iteration 700 / 1000: loss 1.134005\n",
      "iteration 800 / 1000: loss 1.308659\n",
      "iteration 900 / 1000: loss 1.377043\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.303370\n",
      "iteration 200 / 1000: loss 2.245629\n",
      "iteration 300 / 1000: loss 1.933844\n",
      "iteration 400 / 1000: loss 1.679597\n",
      "iteration 500 / 1000: loss 1.534787\n",
      "iteration 600 / 1000: loss 1.365677\n",
      "iteration 700 / 1000: loss 1.519369\n",
      "iteration 800 / 1000: loss 1.383952\n",
      "iteration 900 / 1000: loss 1.456719\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 1.915733\n",
      "iteration 200 / 1000: loss 1.520200\n",
      "iteration 300 / 1000: loss 1.397767\n",
      "iteration 400 / 1000: loss 1.373493\n",
      "iteration 500 / 1000: loss 1.398595\n",
      "iteration 600 / 1000: loss 1.322926\n",
      "iteration 700 / 1000: loss 1.311911\n",
      "iteration 800 / 1000: loss 1.311229\n",
      "iteration 900 / 1000: loss 1.272577\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 1.553962\n",
      "iteration 200 / 1000: loss 1.562445\n",
      "iteration 300 / 1000: loss 1.335725\n",
      "iteration 400 / 1000: loss 1.399271\n",
      "iteration 500 / 1000: loss 1.298785\n",
      "iteration 600 / 1000: loss 1.251411\n",
      "iteration 700 / 1000: loss 1.416741\n",
      "iteration 800 / 1000: loss 1.286595\n",
      "iteration 900 / 1000: loss 1.215512\n",
      "Finished!\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "#hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "#net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "hidden_size = [500,70,90]\n",
    "Learning_rate=[0.1,0.3,0.5]\n",
    "best_acc=-1\n",
    "for i in range(0,3):\n",
    "    for j in range(0,3):\n",
    "        net = TwoLayerNet(input_dim, hidden_size[i], num_classes)\n",
    "        # Train the network\n",
    "        stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "                    num_iters=1000, batch_size=200,\n",
    "                    learning_rate=Learning_rate[j], learning_rate_decay=0.95,\n",
    "                    reg=0.001, verbose=True)\n",
    "        # Predict on the validation set\n",
    "        val_acc = (net.predict(X_val_feats) == y_val).mean()\n",
    "        if val_acc>best_acc:\n",
    "            best_acc=val_acc\n",
    "            best_net=net\n",
    "print(\"Finished!\")\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
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